The invention relates to the field of computerized text analytics.
Text analytics (also “text mining”), is often defined as the computerized process of deriving high-quality of information from text. High-quality information is typically obtained by automatically analyzing patterns and trends through means such as statistical pattern learning. Text analytics commonly involves the process of structuring the input text (usually parsing, along with the addition of some derived linguistic features and the removal of others, and subsequent insertion into a database), deriving patterns within the structured data, and finally evaluation and interpretation of the output.
Typical text analytics tasks include text categorization, text clustering, concept/entity extraction, production of granular taxonomies, sentiment analysis, document summarization, and entity relation modeling.
Text analytics typically involves automatic tasks such as information retrieval, lexical analysis to study word frequency distributions, pattern recognition, tagging/annotation, information extraction, data mining techniques including link and association analysis, visualization, and predictive analytics. The overarching goal is, essentially, to turn text into data for analysis, via application of natural language processing (NLP) and analytical methods.
Text analytics tasks include computer-executed rulesets that cause the computer to analyze the input text in a different way from how a human would have done so. These rulesets also allow the computer to analyze massive amounts of text in very short times, a task not feasible by humans. Sometimes the computer analysis even provides more accurate results than human analysis.
The foregoing examples of the related art and limitations related therewith are intended to be illustrative and not exclusive. Other limitations of the related art will become apparent to those of skill in the art upon a reading of the specification and a study of the figures.